{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings  \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train= pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")\n",
    "train_original=train.copy()\n",
    "test_original=test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.533389</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.231109</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.890349</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0       128.0             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History  Loan_Status  LoanAmount_log  Gender_Female  Gender_Male  \\\n",
       "0               1.0            1        4.852030              0            1   \n",
       "1               1.0            0        4.852030              0            1   \n",
       "2               1.0            1        4.189655              0            1   \n",
       "3               1.0            1        4.787492              0            1   \n",
       "4               1.0            1        4.948760              0            1   \n",
       "..              ...          ...             ...            ...          ...   \n",
       "609             1.0            1        4.262680              1            0   \n",
       "610             1.0            1        3.688879              0            1   \n",
       "611             1.0            1        5.533389              0            1   \n",
       "612             1.0            1        5.231109              0            1   \n",
       "613             0.0            0        4.890349              1            0   \n",
       "\n",
       "     Married_No  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0             1  ...             1             0             0   \n",
       "1             0  ...             0             1             0   \n",
       "2             0  ...             1             0             0   \n",
       "3             0  ...             1             0             0   \n",
       "4             1  ...             1             0             0   \n",
       "..          ...  ...           ...           ...           ...   \n",
       "609           1  ...             1             0             0   \n",
       "610           0  ...             0             0             0   \n",
       "611           0  ...             0             1             0   \n",
       "612           0  ...             0             0             1   \n",
       "613           1  ...             1             0             0   \n",
       "\n",
       "     Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                     1                       0                 1   \n",
       "1                     1                       0                 1   \n",
       "2                     1                       0                 0   \n",
       "3                     0                       1                 1   \n",
       "4                     1                       0                 1   \n",
       "..                  ...                     ...               ...   \n",
       "609                   1                       0                 1   \n",
       "610                   1                       0                 1   \n",
       "611                   1                       0                 1   \n",
       "612                   1                       0                 1   \n",
       "613                   1                       0                 0   \n",
       "\n",
       "     Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                    0                    0                        0   \n",
       "1                    0                    1                        0   \n",
       "2                    1                    0                        0   \n",
       "3                    0                    0                        0   \n",
       "4                    0                    0                        0   \n",
       "..                 ...                  ...                      ...   \n",
       "609                  0                    1                        0   \n",
       "610                  0                    1                        0   \n",
       "611                  0                    0                        0   \n",
       "612                  0                    0                        0   \n",
       "613                  1                    0                        1   \n",
       "\n",
       "     Property_Area_Urban  \n",
       "0                      1  \n",
       "1                      0  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  \n",
       "..                   ...  \n",
       "609                    0  \n",
       "610                    0  \n",
       "611                    1  \n",
       "612                    1  \n",
       "613                    0  \n",
       "\n",
       "[614 rows x 22 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Gender', ['Male', 'Female'], array([489, 112], dtype=int64)],\n",
       " ['Married', ['Yes', 'No'], array([398, 213], dtype=int64)],\n",
       " ['Dependents',\n",
       "  ['0', '1', '2', '3+'],\n",
       "  array([345, 102, 101,  51], dtype=int64)],\n",
       " ['Education', ['Graduate', 'Not Graduate'], array([480, 134], dtype=int64)],\n",
       " ['Self_Employed', ['No', 'Yes'], array([500,  82], dtype=int64)],\n",
       " ['Credit_History', [1.0, 0.0], array([475,  89], dtype=int64)],\n",
       " ['Property_Area',\n",
       "  ['Semiurban', 'Urban', 'Rural'],\n",
       "  array([233, 202, 179], dtype=int64)],\n",
       " ['Loan_Status', ['Y', 'N'], array([422, 192], dtype=int64)]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D = []\n",
    "for k, v in train.iteritems():\n",
    "        item = v.value_counts()\n",
    "        if len(item)<=8: D.append([k,item.index.tolist(),item.values])\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#以下开始数据可视化描述变量关系\n",
    "train[\"Loan_Status\"].value_counts(normalize=\"true\").plot.bar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Independent Variable (Categorical) \n",
    "\n",
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(221) \n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender') \n",
    "\n",
    "plt.subplot(222)\n",
    "train['Married'].value_counts(normalize=True).plot.bar(title= 'Married') \n",
    "\n",
    "plt.subplot(223) \n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self_Employed') \n",
    "\n",
    "plt.subplot(224) \n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(title= 'Credit_History') \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Independent Variable (Ordinal)\n",
    "\n",
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(131) \n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6), title= 'Dependents') \n",
    "\n",
    "plt.subplot(132)\n",
    "train['Education'].value_counts(normalize=True).plot.bar(title= 'Education') \n",
    "\n",
    "plt.subplot(133) \n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area') \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Numerical_Variable (ApplicantIncome)\n",
    "\n",
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(121) \n",
    "sns.distplot(train['ApplicantIncome']); \n",
    "\n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ApplicantIncome_by_Education\n",
    "\n",
    "train.boxplot(column='ApplicantIncome', by = 'Education'),\n",
    "p=plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Numerical_Variable (CoapplicantIncome)\n",
    "\n",
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.distplot(train[\"CoapplicantIncome\"])\n",
    "\n",
    "plt.subplot(122)\n",
    "train[\"CoapplicantIncome\"].plot.box(figsize=(14,6))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00        0.00\n",
       "0.25        0.00\n",
       "0.50     1188.50\n",
       "0.75     2297.25\n",
       "1.00    41667.00\n",
       "Name: CoapplicantIncome, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['CoapplicantIncome'].quantile([0,0.25,0.50,0.75,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Numerical_Variable (LoanAmount)\n",
    "\n",
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(121)\n",
    "df=train.dropna()\n",
    "sns.distplot(df['LoanAmount']); \n",
    "\n",
    "plt.subplot(122) \n",
    "train['LoanAmount'].plot.box(figsize=(16,5)) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender vs Loan_status\n",
    "Gender = pd.crosstab(train['Gender'], train['Loan_Status']).apply(lambda x : x/x.sum(),axis=1 )\n",
    "Gender.div(Gender.sum(1).astype(float),axis=0).plot(kind=\"bar\", stacked=True, figsize=(10,4))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Married vs Loan_Status\n",
    "\n",
    "Married = pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "Married.div(Married.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True,figsize=(10,4))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dependents vs Loan_Status\n",
    "\n",
    "Dependents = pd.crosstab(train['Dependents'],train['Loan_Status'])\n",
    "Dependents.div(Dependents.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True,figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Education vs Loan_Status\n",
    "\n",
    "Education = pd.crosstab(train['Education'],train['Loan_Status'])\n",
    "Education.div(Education.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True,figsize=(10,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Self_Employed vs Loan_Status\n",
    "\n",
    "Self_Employed = pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float),axis=0).plot(kind=\"bar\",stacked=True,figsize=(10,4))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Credit_History vs Loan_Status\n",
    "\n",
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status']) \n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(10,4))\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Property_Area vs Loan_Status\n",
    "Property_Area=pd.crosstab(train['Property_Area'],train['Loan_Status']) \n",
    "Property_Area.div(Property_Area.sum(1).astype(float),axis=0).plot(kind=\"bar\", stacked=True, figsize=(15,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ApplicantIncome vs Loan_Status\n",
    "\n",
    "train.groupby('Loan_Status')['ApplicantIncome'].mean()\n",
    "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income_bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>34</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>67</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very high</th>\n",
       "      <td>46</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Loan_Status   N    Y\n",
       "Income_bin          \n",
       "Low          34   74\n",
       "Average      67  159\n",
       "High         45   98\n",
       "Very high    46   91"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=[0,2500,4000,6000,81000] \n",
    "group=['Low','Average','High', 'Very high'] \n",
    "train['Income_bin']=pd.cut(train['ApplicantIncome'],bins,labels=group)\n",
    "train['Income_bin']\n",
    "Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0)\n",
    "Income_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,2500,4000,6000,81000] \n",
    "group=['Low','Average','High', 'Very high'] \n",
    "train['Income_bin']=pd.cut(train['ApplicantIncome'],bins,labels=group)\n",
    "train['Income_bin']\n",
    "Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True,figsize=(15,5))\n",
    "plt.xlabel('ApplicantIncome')\n",
    "p=plt.ylabel('Percentage')\n",
    "#plt.ylabel('Percentage')\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CoapplicantIncome vs Loan_Status\n",
    "\n",
    "bins=[0,1000,3000,42000]\n",
    "groups=['Low','Average','High']\n",
    "train['CoapplicantIncome_bin']=pd.cut(train['CoapplicantIncome'],bins,labels=groups)\n",
    "Coapplicant_bin=pd.crosstab(train[\"CoapplicantIncome_bin\"],train['Loan_Status'])\n",
    "Coapplicant_bin.div(Coapplicant_bin.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True,figsize=(10,4))\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "#p=plt.ylabel('Percentage')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total_Income vs Loan_Status\n",
    "\n",
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "bins=[0,2500,4000,6000,81000]\n",
    "groups=['Low','Average','High','Very High']\n",
    "train['TotalIncome_bin']= pd.cut(train['Total_Income'],bins,labels=groups)\n",
    "TotalIncome_bin= pd.crosstab(train['TotalIncome_bin'],train['Loan_Status'])\n",
    "TotalIncome_bin.div(TotalIncome_bin.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True,figsize=(10,4))\n",
    "plt.xlabel('Total_Income')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LoanAmount vs Loan_Status\n",
    "\n",
    "bins=[0,100,200,700]\n",
    "group=['Low','Average','High']\n",
    "train['LoanAmount_bin']=pd.cut(train['LoanAmount'],bins,labels=group)\n",
    "LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
    "#LoanAmount_bin.apply(lambda x : x/x.sum(),axis=1)\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('Loan_Amount')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除之前因为画图而产生的“特征bin”的多余特征.\n",
    "\n",
    "train=train.drop(['Income_bin','CoapplicantIncome_bin','LoanAmount_bin','TotalIncome_bin','Total_Income'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#虚拟编码特征\n",
    "train['Dependents'].replace('3+',3, inplace=True)\n",
    "test['Dependents'].replace('3+',3, inplace=True)\n",
    "train['Loan_Status'].replace('N',0, inplace=True)\n",
    "train['Loan_Status'].replace('Y',1, inplace=True)\n",
    "matrix = train.corr() \n",
    "plt.subplots(figsize=(9, 6)) \n",
    "sns.heatmap(matrix, vmax=0.8, square=True, cmap=\"BuPu\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看缺失值\n",
    "\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender                0\n",
       "Married               0\n",
       "Dependents            0\n",
       "Education             0\n",
       "Self_Employed         0\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练集用众数填补\n",
    "train['Gender'].fillna(train['Gender'].mode()[0],inplace=True)\n",
    "train['Married'].fillna(train['Married'].mode()[0],inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0],inplace=True)\n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0],inplace=True)\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0],inplace=True)\n",
    "train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "240.0      4\n",
       "84.0       4\n",
       "120.0      3\n",
       "60.0       2\n",
       "36.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    526\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "240.0      4\n",
       "84.0       4\n",
       "120.0      3\n",
       "60.0       2\n",
       "36.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0],inplace=True)\n",
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(),inplace=True)\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#测试集用众数填补\n",
    "test['Gender'].fillna(test['Gender'].mode()[0], inplace=True) \n",
    "test['Married'].fillna(test['Married'].mode()[0],inplace=True)\n",
    "test['Dependents'].fillna(test['Dependents'].mode()[0], inplace=True)\n",
    "test['Self_Employed'].fillna(test['Self_Employed'].mode()[0], inplace=True)\n",
    "test['Credit_History'].fillna(test['Credit_History'].mode()[0], inplace=True)\n",
    "test['Loan_Amount_Term'].fillna(test['Loan_Amount_Term'].mode()[0], inplace=True) \n",
    "test['LoanAmount'].fillna(test['LoanAmount'].median(), inplace=True)\n",
    "\n",
    "test.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为了构建逻辑回归函数，将变量取对数\n",
    "train['LoanAmount_log']=np.log(train['LoanAmount'])\n",
    "test['LoanAmount_log']=np.log(test['LoanAmount'])\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将 Loan ID 特征去掉，因为不影响预测结果\n",
    "\n",
    "train = train.drop('Loan_ID',axis=1)\n",
    "test = test.drop('Loan_ID',axis=1)\n",
    "x= train.drop('Loan_Status',axis=1)\n",
    "y= train.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dummy Variables（虚拟变量），对于无序多分类变量，引入模型时需要转化为哑变量\n",
    "\n",
    "x=pd.get_dummies(x) \n",
    "train=pd.get_dummies(train)\n",
    "test=pd.get_dummies(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ApplicantIncome',\n",
       " 'CoapplicantIncome',\n",
       " 'LoanAmount',\n",
       " 'Loan_Amount_Term',\n",
       " 'Credit_History',\n",
       " 'LoanAmount_log',\n",
       " 'Gender_Female',\n",
       " 'Gender_Male',\n",
       " 'Married_No',\n",
       " 'Married_Yes',\n",
       " 'Dependents_3',\n",
       " 'Dependents_0',\n",
       " 'Dependents_1',\n",
       " 'Dependents_2',\n",
       " 'Education_Graduate',\n",
       " 'Education_Not Graduate',\n",
       " 'Self_Employed_No',\n",
       " 'Self_Employed_Yes',\n",
       " 'Property_Area_Rural',\n",
       " 'Property_Area_Semiurban',\n",
       " 'Property_Area_Urban']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.corr().index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0,\n",
       "  -0.11660458122889955,\n",
       "  0.5651805176233123,\n",
       "  -0.046531121839299014,\n",
       "  -0.018615389223707195,\n",
       "  0.4353923000523801,\n",
       "  -0.05880897972216586,\n",
       "  0.05880897972216575,\n",
       "  -0.05170837562185542,\n",
       "  0.05170837562185544,\n",
       "  0.15668677928328717,\n",
       "  -0.09259904444172608,\n",
       "  0.04086148982305024,\n",
       "  -0.0346503126943075,\n",
       "  0.14076027561439775,\n",
       "  -0.1407602756143978,\n",
       "  -0.12717953412249886,\n",
       "  0.12717953412249897,\n",
       "  0.015829179740975436,\n",
       "  -0.01424594560150332,\n",
       "  -0.0005978499977315698],\n",
       " [-0.11660458122889955,\n",
       "  1.0,\n",
       "  0.18921778196662536,\n",
       "  -0.05938308792710458,\n",
       "  0.011133917739935224,\n",
       "  0.20632964429064085,\n",
       "  -0.08291207109150525,\n",
       "  0.0829120710915052,\n",
       "  -0.07594767166668794,\n",
       "  0.07594767166668798,\n",
       "  0.04149080838925088,\n",
       "  -0.008292006472146982,\n",
       "  -0.029769111556922218,\n",
       "  0.010015832080627602,\n",
       "  0.06228982561819101,\n",
       "  -0.06228982561819105,\n",
       "  0.0161001068048997,\n",
       "  -0.016100106804899702,\n",
       "  0.005329339116031553,\n",
       "  -0.02704357872478651,\n",
       "  0.022775703815366314],\n",
       " [0.5651805176233123,\n",
       "  0.18921778196662536,\n",
       "  1.0,\n",
       "  0.03696026297885571,\n",
       "  -0.0006073272487502419,\n",
       "  0.8957078290749446,\n",
       "  -0.1069042865088233,\n",
       "  0.1069042865088233,\n",
       "  -0.14654570094795913,\n",
       "  0.1465457009479589,\n",
       "  0.1528498474284586,\n",
       "  -0.14763909392043315,\n",
       "  0.06197606831491383,\n",
       "  0.020126378470113903,\n",
       "  0.16875909051627078,\n",
       "  -0.16875909051627083,\n",
       "  -0.1150995570369231,\n",
       "  0.11509955703692307,\n",
       "  0.04346672240754546,\n",
       "  -0.005803676615521412,\n",
       "  -0.0360499364513647],\n",
       " [-0.046531121839299014,\n",
       "  -0.05938308792710458,\n",
       "  0.03696026297885571,\n",
       "  1.0,\n",
       "  -0.004704983284487817,\n",
       "  0.08535299711042586,\n",
       "  0.07402955695274217,\n",
       "  -0.07402955695274216,\n",
       "  0.10091179393444091,\n",
       "  -0.10091179393444098,\n",
       "  -0.07727305909684012,\n",
       "  0.1181630051031619,\n",
       "  -0.08849227083281966,\n",
       "  -0.010608504135744339,\n",
       "  0.07392786056480716,\n",
       "  -0.07392786056480717,\n",
       "  0.03373943086333154,\n",
       "  -0.03373943086333154,\n",
       "  0.0343210912431727,\n",
       "  0.05914116201122619,\n",
       "  -0.09427867741553055],\n",
       " [-0.018615389223707195,\n",
       "  0.011133917739935224,\n",
       "  -0.0006073272487502419,\n",
       "  -0.004704983284487817,\n",
       "  1.0,\n",
       "  -0.018547061550233255,\n",
       "  -0.009169852141934783,\n",
       "  0.009169852141934751,\n",
       "  -0.010938087779501355,\n",
       "  0.010938087779501312,\n",
       "  -0.06047275994899768,\n",
       "  0.020500385821553946,\n",
       "  0.009757472070526513,\n",
       "  0.007987272950391092,\n",
       "  0.0736577827752733,\n",
       "  -0.07365778277527331,\n",
       "  0.0015504602720603502,\n",
       "  -0.0015504602720603441,\n",
       "  -0.020905920164527223,\n",
       "  0.03597583895156686,\n",
       "  -0.016934211142743266],\n",
       " [0.4353923000523801,\n",
       "  0.20632964429064085,\n",
       "  0.8957078290749446,\n",
       "  0.08535299711042586,\n",
       "  -0.018547061550233255,\n",
       "  1.0,\n",
       "  -0.1433883698696578,\n",
       "  0.1433883698696578,\n",
       "  -0.17980186588311947,\n",
       "  0.17980186588311958,\n",
       "  0.12367345932921311,\n",
       "  -0.14761878141773774,\n",
       "  0.05754216187710191,\n",
       "  0.046270432526731284,\n",
       "  0.14588487757196988,\n",
       "  -0.14588487757196988,\n",
       "  -0.1096767315229324,\n",
       "  0.10967673152293243,\n",
       "  0.07861359258383536,\n",
       "  0.007196587126879432,\n",
       "  -0.08347305180999806],\n",
       " [-0.05880897972216586,\n",
       "  -0.08291207109150525,\n",
       "  -0.1069042865088233,\n",
       "  0.07402955695274217,\n",
       "  -0.009169852141934783,\n",
       "  -0.1433883698696578,\n",
       "  1.0,\n",
       "  -1.0,\n",
       "  0.36456859515764434,\n",
       "  -0.364568595157644,\n",
       "  -0.09631874375652186,\n",
       "  0.14842063180579818,\n",
       "  0.0044660073111407505,\n",
       "  -0.12995276433819022,\n",
       "  0.04536390979706779,\n",
       "  -0.04536390979706775,\n",
       "  -0.0005249884341662623,\n",
       "  0.0005249884341662705,\n",
       "  -0.08028345470353294,\n",
       "  0.10862267070999936,\n",
       "  -0.03452985309134903],\n",
       " [0.05880897972216575,\n",
       "  0.0829120710915052,\n",
       "  0.1069042865088233,\n",
       "  -0.07402955695274216,\n",
       "  0.009169852141934751,\n",
       "  0.1433883698696578,\n",
       "  -1.0,\n",
       "  1.0,\n",
       "  -0.36456859515764417,\n",
       "  0.3645685951576439,\n",
       "  0.09631874375652182,\n",
       "  -0.14842063180579815,\n",
       "  -0.004466007311140782,\n",
       "  0.12995276433819022,\n",
       "  -0.045363909797067746,\n",
       "  0.04536390979706772,\n",
       "  0.0005249884341662387,\n",
       "  -0.0005249884341662938,\n",
       "  0.08028345470353289,\n",
       "  -0.10862267070999938,\n",
       "  0.03452985309134903],\n",
       " [-0.05170837562185542,\n",
       "  -0.07594767166668794,\n",
       "  -0.14654570094795913,\n",
       "  0.10091179393444091,\n",
       "  -0.010938087779501355,\n",
       "  -0.17980186588311947,\n",
       "  0.36456859515764434,\n",
       "  -0.36456859515764417,\n",
       "  1.0,\n",
       "  -1.0000000000000004,\n",
       "  -0.1325663982503919,\n",
       "  0.3481748518922105,\n",
       "  -0.11385315077526983,\n",
       "  -0.24954730962757743,\n",
       "  0.012304382757047516,\n",
       "  -0.012304382757047538,\n",
       "  0.004488754282274508,\n",
       "  -0.0044887542822744635,\n",
       "  0.006805497066597856,\n",
       "  -0.0058454149498430325,\n",
       "  -0.0005455964689105307],\n",
       " [0.05170837562185544,\n",
       "  0.07594767166668798,\n",
       "  0.1465457009479589,\n",
       "  -0.10091179393444098,\n",
       "  0.010938087779501312,\n",
       "  0.17980186588311958,\n",
       "  -0.364568595157644,\n",
       "  0.3645685951576439,\n",
       "  -1.0000000000000004,\n",
       "  1.0,\n",
       "  0.13256639825039185,\n",
       "  -0.3481748518922104,\n",
       "  0.11385315077526993,\n",
       "  0.24954730962757762,\n",
       "  -0.012304382757047552,\n",
       "  0.012304382757047556,\n",
       "  -0.0044887542822745485,\n",
       "  0.0044887542822745295,\n",
       "  -0.006805497066597734,\n",
       "  0.005845414949843076,\n",
       "  0.0005455964689103537],\n",
       " [0.15668677928328717,\n",
       "  0.04149080838925088,\n",
       "  0.1528498474284586,\n",
       "  -0.07727305909684012,\n",
       "  -0.06047275994899768,\n",
       "  0.12367345932921311,\n",
       "  -0.09631874375652186,\n",
       "  0.09631874375652182,\n",
       "  -0.1325663982503919,\n",
       "  0.13256639825039185,\n",
       "  1.0,\n",
       "  -0.3583152421121548,\n",
       "  -0.13433710843759739,\n",
       "  -0.13354661823011826,\n",
       "  -0.05528841412620542,\n",
       "  0.055288414126205436,\n",
       "  -0.003277600079000044,\n",
       "  0.003277600079000074,\n",
       "  0.04066950202960677,\n",
       "  0.007863407328746238,\n",
       "  -0.04745963640214606],\n",
       " [-0.09259904444172608,\n",
       "  -0.008292006472146982,\n",
       "  -0.14763909392043315,\n",
       "  0.1181630051031619,\n",
       "  0.020500385821553946,\n",
       "  -0.14761878141773774,\n",
       "  0.14842063180579818,\n",
       "  -0.14842063180579815,\n",
       "  0.3481748518922105,\n",
       "  -0.3481748518922104,\n",
       "  -0.3583152421121548,\n",
       "  1.0,\n",
       "  -0.5313730172530097,\n",
       "  -0.5282462180272226,\n",
       "  0.036562584129815924,\n",
       "  -0.03656258412981593,\n",
       "  0.088254348508804,\n",
       "  -0.088254348508804,\n",
       "  0.04401519667190941,\n",
       "  -0.004173296949485654,\n",
       "  -0.03826431392741704],\n",
       " [0.04086148982305024,\n",
       "  -0.029769111556922218,\n",
       "  0.06197606831491383,\n",
       "  -0.08849227083281966,\n",
       "  0.009757472070526513,\n",
       "  0.05754216187710191,\n",
       "  0.0044660073111407505,\n",
       "  -0.004466007311140782,\n",
       "  -0.11385315077526983,\n",
       "  0.11385315077526993,\n",
       "  -0.13433710843759739,\n",
       "  -0.5313730172530097,\n",
       "  1.0,\n",
       "  -0.19804647174529566,\n",
       "  0.013354661253674507,\n",
       "  -0.013354661253674488,\n",
       "  -0.0820436313318516,\n",
       "  0.0820436313318516,\n",
       "  -0.08411676437991847,\n",
       "  0.011661246821471934,\n",
       "  0.06931972656680993],\n",
       " [-0.0346503126943075,\n",
       "  0.010015832080627602,\n",
       "  0.020126378470113903,\n",
       "  -0.010608504135744339,\n",
       "  0.007987272950391092,\n",
       "  0.046270432526731284,\n",
       "  -0.12995276433819022,\n",
       "  0.12995276433819022,\n",
       "  -0.24954730962757743,\n",
       "  0.24954730962757762,\n",
       "  -0.13354661823011826,\n",
       "  -0.5282462180272226,\n",
       "  -0.19804647174529566,\n",
       "  1.0,\n",
       "  -0.02082150321575009,\n",
       "  0.020821503215750055,\n",
       "  -0.032434130660608386,\n",
       "  0.03243413066060838,\n",
       "  -0.004298056632867181,\n",
       "  -0.012017047863104747,\n",
       "  0.016568577867511675],\n",
       " [0.14076027561439775,\n",
       "  0.06228982561819101,\n",
       "  0.16875909051627078,\n",
       "  0.07392786056480716,\n",
       "  0.0736577827752733,\n",
       "  0.14588487757196988,\n",
       "  0.04536390979706779,\n",
       "  -0.045363909797067746,\n",
       "  0.012304382757047516,\n",
       "  -0.012304382757047552,\n",
       "  -0.05528841412620542,\n",
       "  0.036562584129815924,\n",
       "  0.013354661253674507,\n",
       "  -0.02082150321575009,\n",
       "  1.0,\n",
       "  -1.0,\n",
       "  -0.010383097430977824,\n",
       "  0.01038309743097775,\n",
       "  -0.07751962112239343,\n",
       "  0.039410434881761096,\n",
       "  0.03427916397939333],\n",
       " [-0.1407602756143978,\n",
       "  -0.06228982561819105,\n",
       "  -0.16875909051627083,\n",
       "  -0.07392786056480717,\n",
       "  -0.07365778277527331,\n",
       "  -0.14588487757196988,\n",
       "  -0.04536390979706775,\n",
       "  0.04536390979706772,\n",
       "  -0.012304382757047538,\n",
       "  0.012304382757047556,\n",
       "  0.055288414126205436,\n",
       "  -0.03656258412981593,\n",
       "  -0.013354661253674488,\n",
       "  0.020821503215750055,\n",
       "  -1.0,\n",
       "  1.0,\n",
       "  0.01038309743097779,\n",
       "  -0.010383097430977761,\n",
       "  0.07751962112239341,\n",
       "  -0.03941043488176106,\n",
       "  -0.034279163979393325],\n",
       " [-0.12717953412249886,\n",
       "  0.0161001068048997,\n",
       "  -0.1150995570369231,\n",
       "  0.03373943086333154,\n",
       "  0.0015504602720603502,\n",
       "  -0.1096767315229324,\n",
       "  -0.0005249884341662623,\n",
       "  0.0005249884341662387,\n",
       "  0.004488754282274508,\n",
       "  -0.0044887542822745485,\n",
       "  -0.003277600079000044,\n",
       "  0.088254348508804,\n",
       "  -0.0820436313318516,\n",
       "  -0.032434130660608386,\n",
       "  -0.010383097430977824,\n",
       "  0.01038309743097779,\n",
       "  1.0,\n",
       "  -1.0,\n",
       "  -0.022065160066500895,\n",
       "  -0.008709557603406018,\n",
       "  0.030338154334301776],\n",
       " [0.12717953412249897,\n",
       "  -0.016100106804899702,\n",
       "  0.11509955703692307,\n",
       "  -0.03373943086333154,\n",
       "  -0.0015504602720603441,\n",
       "  0.10967673152293243,\n",
       "  0.0005249884341662705,\n",
       "  -0.0005249884341662938,\n",
       "  -0.0044887542822744635,\n",
       "  0.0044887542822745295,\n",
       "  0.003277600079000074,\n",
       "  -0.088254348508804,\n",
       "  0.0820436313318516,\n",
       "  0.03243413066060838,\n",
       "  0.01038309743097775,\n",
       "  -0.010383097430977761,\n",
       "  -1.0,\n",
       "  1.0,\n",
       "  0.022065160066500993,\n",
       "  0.008709557603406094,\n",
       "  -0.030338154334301825],\n",
       " [0.015829179740975436,\n",
       "  0.005329339116031553,\n",
       "  0.04346672240754546,\n",
       "  0.0343210912431727,\n",
       "  -0.020905920164527223,\n",
       "  0.07861359258383536,\n",
       "  -0.08028345470353294,\n",
       "  0.08028345470353289,\n",
       "  0.006805497066597856,\n",
       "  -0.006805497066597734,\n",
       "  0.04066950202960677,\n",
       "  0.04401519667190941,\n",
       "  -0.08411676437991847,\n",
       "  -0.004298056632867181,\n",
       "  -0.07751962112239343,\n",
       "  0.07751962112239341,\n",
       "  -0.022065160066500895,\n",
       "  0.022065160066500993,\n",
       "  1.0,\n",
       "  -0.5016460069852571,\n",
       "  -0.4491681607182965],\n",
       " [-0.01424594560150332,\n",
       "  -0.02704357872478651,\n",
       "  -0.005803676615521412,\n",
       "  0.05914116201122619,\n",
       "  0.03597583895156686,\n",
       "  0.007196587126879432,\n",
       "  0.10862267070999936,\n",
       "  -0.10862267070999938,\n",
       "  -0.0058454149498430325,\n",
       "  0.005845414949843076,\n",
       "  0.007863407328746238,\n",
       "  -0.004173296949485654,\n",
       "  0.011661246821471934,\n",
       "  -0.012017047863104747,\n",
       "  0.039410434881761096,\n",
       "  -0.03941043488176106,\n",
       "  -0.008709557603406018,\n",
       "  0.008709557603406094,\n",
       "  -0.5016460069852571,\n",
       "  1.0,\n",
       "  -0.5475736604235864],\n",
       " [-0.0005978499977315698,\n",
       "  0.022775703815366314,\n",
       "  -0.0360499364513647,\n",
       "  -0.09427867741553055,\n",
       "  -0.016934211142743266,\n",
       "  -0.08347305180999806,\n",
       "  -0.03452985309134903,\n",
       "  0.03452985309134903,\n",
       "  -0.0005455964689105307,\n",
       "  0.0005455964689103537,\n",
       "  -0.04745963640214606,\n",
       "  -0.03826431392741704,\n",
       "  0.06931972656680993,\n",
       "  0.016568577867511675,\n",
       "  0.03427916397939333,\n",
       "  -0.034279163979393325,\n",
       "  0.030338154334301776,\n",
       "  -0.030338154334301825,\n",
       "  -0.4491681607182965,\n",
       "  -0.5475736604235864,\n",
       "  1.0]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = x.corr().values.tolist()\n",
    "\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 1.0],\n",
       " [0, 1, -0.11660458122889955],\n",
       " [0, 2, 0.5651805176233123],\n",
       " [0, 3, -0.046531121839299014],\n",
       " [0, 4, -0.018615389223707195],\n",
       " [0, 5, 0.4353923000523801],\n",
       " [0, 6, -0.05880897972216586],\n",
       " [0, 7, 0.05880897972216575],\n",
       " [0, 8, -0.05170837562185542],\n",
       " [0, 9, 0.05170837562185544],\n",
       " [0, 10, 0.15668677928328717],\n",
       " [0, 11, -0.09259904444172608],\n",
       " [0, 12, 0.04086148982305024],\n",
       " [0, 13, -0.0346503126943075],\n",
       " [0, 14, 0.14076027561439775],\n",
       " [0, 15, -0.1407602756143978],\n",
       " [0, 16, -0.12717953412249886],\n",
       " [0, 17, 0.12717953412249897],\n",
       " [0, 18, 0.015829179740975436],\n",
       " [0, 19, -0.01424594560150332],\n",
       " [0, 20, -0.0005978499977315698],\n",
       " [1, 0, -0.11660458122889955],\n",
       " [1, 1, 1.0],\n",
       " [1, 2, 0.18921778196662536],\n",
       " [1, 3, -0.05938308792710458],\n",
       " [1, 4, 0.011133917739935224],\n",
       " [1, 5, 0.20632964429064085],\n",
       " [1, 6, -0.08291207109150525],\n",
       " [1, 7, 0.0829120710915052],\n",
       " [1, 8, -0.07594767166668794],\n",
       " [1, 9, 0.07594767166668798],\n",
       " [1, 10, 0.04149080838925088],\n",
       " [1, 11, -0.008292006472146982],\n",
       " [1, 12, -0.029769111556922218],\n",
       " [1, 13, 0.010015832080627602],\n",
       " [1, 14, 0.06228982561819101],\n",
       " [1, 15, -0.06228982561819105],\n",
       " [1, 16, 0.0161001068048997],\n",
       " [1, 17, -0.016100106804899702],\n",
       " [1, 18, 0.005329339116031553],\n",
       " [1, 19, -0.02704357872478651],\n",
       " [1, 20, 0.022775703815366314],\n",
       " [2, 0, 0.5651805176233123],\n",
       " [2, 1, 0.18921778196662536],\n",
       " [2, 2, 1.0],\n",
       " [2, 3, 0.03696026297885571],\n",
       " [2, 4, -0.0006073272487502419],\n",
       " [2, 5, 0.8957078290749446],\n",
       " [2, 6, -0.1069042865088233],\n",
       " [2, 7, 0.1069042865088233],\n",
       " [2, 8, -0.14654570094795913],\n",
       " [2, 9, 0.1465457009479589],\n",
       " [2, 10, 0.1528498474284586],\n",
       " [2, 11, -0.14763909392043315],\n",
       " [2, 12, 0.06197606831491383],\n",
       " [2, 13, 0.020126378470113903],\n",
       " [2, 14, 0.16875909051627078],\n",
       " [2, 15, -0.16875909051627083],\n",
       " [2, 16, -0.1150995570369231],\n",
       " [2, 17, 0.11509955703692307],\n",
       " [2, 18, 0.04346672240754546],\n",
       " [2, 19, -0.005803676615521412],\n",
       " [2, 20, -0.0360499364513647],\n",
       " [3, 0, -0.046531121839299014],\n",
       " [3, 1, -0.05938308792710458],\n",
       " [3, 2, 0.03696026297885571],\n",
       " [3, 3, 1.0],\n",
       " [3, 4, -0.004704983284487817],\n",
       " [3, 5, 0.08535299711042586],\n",
       " [3, 6, 0.07402955695274217],\n",
       " [3, 7, -0.07402955695274216],\n",
       " [3, 8, 0.10091179393444091],\n",
       " [3, 9, -0.10091179393444098],\n",
       " [3, 10, -0.07727305909684012],\n",
       " [3, 11, 0.1181630051031619],\n",
       " [3, 12, -0.08849227083281966],\n",
       " [3, 13, -0.010608504135744339],\n",
       " [3, 14, 0.07392786056480716],\n",
       " [3, 15, -0.07392786056480717],\n",
       " [3, 16, 0.03373943086333154],\n",
       " [3, 17, -0.03373943086333154],\n",
       " [3, 18, 0.0343210912431727],\n",
       " [3, 19, 0.05914116201122619],\n",
       " [3, 20, -0.09427867741553055],\n",
       " [4, 0, -0.018615389223707195],\n",
       " [4, 1, 0.011133917739935224],\n",
       " [4, 2, -0.0006073272487502419],\n",
       " [4, 3, -0.004704983284487817],\n",
       " [4, 4, 1.0],\n",
       " [4, 5, -0.018547061550233255],\n",
       " [4, 6, -0.009169852141934783],\n",
       " [4, 7, 0.009169852141934751],\n",
       " [4, 8, -0.010938087779501355],\n",
       " [4, 9, 0.010938087779501312],\n",
       " [4, 10, -0.06047275994899768],\n",
       " [4, 11, 0.020500385821553946],\n",
       " [4, 12, 0.009757472070526513],\n",
       " [4, 13, 0.007987272950391092],\n",
       " [4, 14, 0.0736577827752733],\n",
       " [4, 15, -0.07365778277527331],\n",
       " [4, 16, 0.0015504602720603502],\n",
       " [4, 17, -0.0015504602720603441],\n",
       " [4, 18, -0.020905920164527223],\n",
       " [4, 19, 0.03597583895156686],\n",
       " [4, 20, -0.016934211142743266],\n",
       " [5, 0, 0.4353923000523801],\n",
       " [5, 1, 0.20632964429064085],\n",
       " [5, 2, 0.8957078290749446],\n",
       " [5, 3, 0.08535299711042586],\n",
       " [5, 4, -0.018547061550233255],\n",
       " [5, 5, 1.0],\n",
       " [5, 6, -0.1433883698696578],\n",
       " [5, 7, 0.1433883698696578],\n",
       " [5, 8, -0.17980186588311947],\n",
       " [5, 9, 0.17980186588311958],\n",
       " [5, 10, 0.12367345932921311],\n",
       " [5, 11, -0.14761878141773774],\n",
       " [5, 12, 0.05754216187710191],\n",
       " [5, 13, 0.046270432526731284],\n",
       " [5, 14, 0.14588487757196988],\n",
       " [5, 15, -0.14588487757196988],\n",
       " [5, 16, -0.1096767315229324],\n",
       " [5, 17, 0.10967673152293243],\n",
       " [5, 18, 0.07861359258383536],\n",
       " [5, 19, 0.007196587126879432],\n",
       " [5, 20, -0.08347305180999806],\n",
       " [6, 0, -0.05880897972216586],\n",
       " [6, 1, -0.08291207109150525],\n",
       " [6, 2, -0.1069042865088233],\n",
       " [6, 3, 0.07402955695274217],\n",
       " [6, 4, -0.009169852141934783],\n",
       " [6, 5, -0.1433883698696578],\n",
       " [6, 6, 1.0],\n",
       " [6, 7, -1.0],\n",
       " [6, 8, 0.36456859515764434],\n",
       " [6, 9, -0.364568595157644],\n",
       " [6, 10, -0.09631874375652186],\n",
       " [6, 11, 0.14842063180579818],\n",
       " [6, 12, 0.0044660073111407505],\n",
       " [6, 13, -0.12995276433819022],\n",
       " [6, 14, 0.04536390979706779],\n",
       " [6, 15, -0.04536390979706775],\n",
       " [6, 16, -0.0005249884341662623],\n",
       " [6, 17, 0.0005249884341662705],\n",
       " [6, 18, -0.08028345470353294],\n",
       " [6, 19, 0.10862267070999936],\n",
       " [6, 20, -0.03452985309134903],\n",
       " [7, 0, 0.05880897972216575],\n",
       " [7, 1, 0.0829120710915052],\n",
       " [7, 2, 0.1069042865088233],\n",
       " [7, 3, -0.07402955695274216],\n",
       " [7, 4, 0.009169852141934751],\n",
       " [7, 5, 0.1433883698696578],\n",
       " [7, 6, -1.0],\n",
       " [7, 7, 1.0],\n",
       " [7, 8, -0.36456859515764417],\n",
       " [7, 9, 0.3645685951576439],\n",
       " [7, 10, 0.09631874375652182],\n",
       " [7, 11, -0.14842063180579815],\n",
       " [7, 12, -0.004466007311140782],\n",
       " [7, 13, 0.12995276433819022],\n",
       " [7, 14, -0.045363909797067746],\n",
       " [7, 15, 0.04536390979706772],\n",
       " [7, 16, 0.0005249884341662387],\n",
       " [7, 17, -0.0005249884341662938],\n",
       " [7, 18, 0.08028345470353289],\n",
       " [7, 19, -0.10862267070999938],\n",
       " [7, 20, 0.03452985309134903],\n",
       " [8, 0, -0.05170837562185542],\n",
       " [8, 1, -0.07594767166668794],\n",
       " [8, 2, -0.14654570094795913],\n",
       " [8, 3, 0.10091179393444091],\n",
       " [8, 4, -0.010938087779501355],\n",
       " [8, 5, -0.17980186588311947],\n",
       " [8, 6, 0.36456859515764434],\n",
       " [8, 7, -0.36456859515764417],\n",
       " [8, 8, 1.0],\n",
       " [8, 9, -1.0000000000000004],\n",
       " [8, 10, -0.1325663982503919],\n",
       " [8, 11, 0.3481748518922105],\n",
       " [8, 12, -0.11385315077526983],\n",
       " [8, 13, -0.24954730962757743],\n",
       " [8, 14, 0.012304382757047516],\n",
       " [8, 15, -0.012304382757047538],\n",
       " [8, 16, 0.004488754282274508],\n",
       " [8, 17, -0.0044887542822744635],\n",
       " [8, 18, 0.006805497066597856],\n",
       " [8, 19, -0.0058454149498430325],\n",
       " [8, 20, -0.0005455964689105307],\n",
       " [9, 0, 0.05170837562185544],\n",
       " [9, 1, 0.07594767166668798],\n",
       " [9, 2, 0.1465457009479589],\n",
       " [9, 3, -0.10091179393444098],\n",
       " [9, 4, 0.010938087779501312],\n",
       " [9, 5, 0.17980186588311958],\n",
       " [9, 6, -0.364568595157644],\n",
       " [9, 7, 0.3645685951576439],\n",
       " [9, 8, -1.0000000000000004],\n",
       " [9, 9, 1.0],\n",
       " [9, 10, 0.13256639825039185],\n",
       " [9, 11, -0.3481748518922104],\n",
       " [9, 12, 0.11385315077526993],\n",
       " [9, 13, 0.24954730962757762],\n",
       " [9, 14, -0.012304382757047552],\n",
       " [9, 15, 0.012304382757047556],\n",
       " [9, 16, -0.0044887542822745485],\n",
       " [9, 17, 0.0044887542822745295],\n",
       " [9, 18, -0.006805497066597734],\n",
       " [9, 19, 0.005845414949843076],\n",
       " [9, 20, 0.0005455964689103537],\n",
       " [10, 0, 0.15668677928328717],\n",
       " [10, 1, 0.04149080838925088],\n",
       " [10, 2, 0.1528498474284586],\n",
       " [10, 3, -0.07727305909684012],\n",
       " [10, 4, -0.06047275994899768],\n",
       " [10, 5, 0.12367345932921311],\n",
       " [10, 6, -0.09631874375652186],\n",
       " [10, 7, 0.09631874375652182],\n",
       " [10, 8, -0.1325663982503919],\n",
       " [10, 9, 0.13256639825039185],\n",
       " [10, 10, 1.0],\n",
       " [10, 11, -0.3583152421121548],\n",
       " [10, 12, -0.13433710843759739],\n",
       " [10, 13, -0.13354661823011826],\n",
       " [10, 14, -0.05528841412620542],\n",
       " [10, 15, 0.055288414126205436],\n",
       " [10, 16, -0.003277600079000044],\n",
       " [10, 17, 0.003277600079000074],\n",
       " [10, 18, 0.04066950202960677],\n",
       " [10, 19, 0.007863407328746238],\n",
       " [10, 20, -0.04745963640214606],\n",
       " [11, 0, -0.09259904444172608],\n",
       " [11, 1, -0.008292006472146982],\n",
       " [11, 2, -0.14763909392043315],\n",
       " [11, 3, 0.1181630051031619],\n",
       " [11, 4, 0.020500385821553946],\n",
       " [11, 5, -0.14761878141773774],\n",
       " [11, 6, 0.14842063180579818],\n",
       " [11, 7, -0.14842063180579815],\n",
       " [11, 8, 0.3481748518922105],\n",
       " [11, 9, -0.3481748518922104],\n",
       " [11, 10, -0.3583152421121548],\n",
       " [11, 11, 1.0],\n",
       " [11, 12, -0.5313730172530097],\n",
       " [11, 13, -0.5282462180272226],\n",
       " [11, 14, 0.036562584129815924],\n",
       " [11, 15, -0.03656258412981593],\n",
       " [11, 16, 0.088254348508804],\n",
       " [11, 17, -0.088254348508804],\n",
       " [11, 18, 0.04401519667190941],\n",
       " [11, 19, -0.004173296949485654],\n",
       " [11, 20, -0.03826431392741704],\n",
       " [12, 0, 0.04086148982305024],\n",
       " [12, 1, -0.029769111556922218],\n",
       " [12, 2, 0.06197606831491383],\n",
       " [12, 3, -0.08849227083281966],\n",
       " [12, 4, 0.009757472070526513],\n",
       " [12, 5, 0.05754216187710191],\n",
       " [12, 6, 0.0044660073111407505],\n",
       " [12, 7, -0.004466007311140782],\n",
       " [12, 8, -0.11385315077526983],\n",
       " [12, 9, 0.11385315077526993],\n",
       " [12, 10, -0.13433710843759739],\n",
       " [12, 11, -0.5313730172530097],\n",
       " [12, 12, 1.0],\n",
       " [12, 13, -0.19804647174529566],\n",
       " [12, 14, 0.013354661253674507],\n",
       " [12, 15, -0.013354661253674488],\n",
       " [12, 16, -0.0820436313318516],\n",
       " [12, 17, 0.0820436313318516],\n",
       " [12, 18, -0.08411676437991847],\n",
       " [12, 19, 0.011661246821471934],\n",
       " [12, 20, 0.06931972656680993],\n",
       " [13, 0, -0.0346503126943075],\n",
       " [13, 1, 0.010015832080627602],\n",
       " [13, 2, 0.020126378470113903],\n",
       " [13, 3, -0.010608504135744339],\n",
       " [13, 4, 0.007987272950391092],\n",
       " [13, 5, 0.046270432526731284],\n",
       " [13, 6, -0.12995276433819022],\n",
       " [13, 7, 0.12995276433819022],\n",
       " [13, 8, -0.24954730962757743],\n",
       " [13, 9, 0.24954730962757762],\n",
       " [13, 10, -0.13354661823011826],\n",
       " [13, 11, -0.5282462180272226],\n",
       " [13, 12, -0.19804647174529566],\n",
       " [13, 13, 1.0],\n",
       " [13, 14, -0.02082150321575009],\n",
       " [13, 15, 0.020821503215750055],\n",
       " [13, 16, -0.032434130660608386],\n",
       " [13, 17, 0.03243413066060838],\n",
       " [13, 18, -0.004298056632867181],\n",
       " [13, 19, -0.012017047863104747],\n",
       " [13, 20, 0.016568577867511675],\n",
       " [14, 0, 0.14076027561439775],\n",
       " [14, 1, 0.06228982561819101],\n",
       " [14, 2, 0.16875909051627078],\n",
       " [14, 3, 0.07392786056480716],\n",
       " [14, 4, 0.0736577827752733],\n",
       " [14, 5, 0.14588487757196988],\n",
       " [14, 6, 0.04536390979706779],\n",
       " [14, 7, -0.045363909797067746],\n",
       " [14, 8, 0.012304382757047516],\n",
       " [14, 9, -0.012304382757047552],\n",
       " [14, 10, -0.05528841412620542],\n",
       " [14, 11, 0.036562584129815924],\n",
       " [14, 12, 0.013354661253674507],\n",
       " [14, 13, -0.02082150321575009],\n",
       " [14, 14, 1.0],\n",
       " [14, 15, -1.0],\n",
       " [14, 16, -0.010383097430977824],\n",
       " [14, 17, 0.01038309743097775],\n",
       " [14, 18, -0.07751962112239343],\n",
       " [14, 19, 0.039410434881761096],\n",
       " [14, 20, 0.03427916397939333],\n",
       " [15, 0, -0.1407602756143978],\n",
       " [15, 1, -0.06228982561819105],\n",
       " [15, 2, -0.16875909051627083],\n",
       " [15, 3, -0.07392786056480717],\n",
       " [15, 4, -0.07365778277527331],\n",
       " [15, 5, -0.14588487757196988],\n",
       " [15, 6, -0.04536390979706775],\n",
       " [15, 7, 0.04536390979706772],\n",
       " [15, 8, -0.012304382757047538],\n",
       " [15, 9, 0.012304382757047556],\n",
       " [15, 10, 0.055288414126205436],\n",
       " [15, 11, -0.03656258412981593],\n",
       " [15, 12, -0.013354661253674488],\n",
       " [15, 13, 0.020821503215750055],\n",
       " [15, 14, -1.0],\n",
       " [15, 15, 1.0],\n",
       " [15, 16, 0.01038309743097779],\n",
       " [15, 17, -0.010383097430977761],\n",
       " [15, 18, 0.07751962112239341],\n",
       " [15, 19, -0.03941043488176106],\n",
       " [15, 20, -0.034279163979393325],\n",
       " [16, 0, -0.12717953412249886],\n",
       " [16, 1, 0.0161001068048997],\n",
       " [16, 2, -0.1150995570369231],\n",
       " [16, 3, 0.03373943086333154],\n",
       " [16, 4, 0.0015504602720603502],\n",
       " [16, 5, -0.1096767315229324],\n",
       " [16, 6, -0.0005249884341662623],\n",
       " [16, 7, 0.0005249884341662387],\n",
       " [16, 8, 0.004488754282274508],\n",
       " [16, 9, -0.0044887542822745485],\n",
       " [16, 10, -0.003277600079000044],\n",
       " [16, 11, 0.088254348508804],\n",
       " [16, 12, -0.0820436313318516],\n",
       " [16, 13, -0.032434130660608386],\n",
       " [16, 14, -0.010383097430977824],\n",
       " [16, 15, 0.01038309743097779],\n",
       " [16, 16, 1.0],\n",
       " [16, 17, -1.0],\n",
       " [16, 18, -0.022065160066500895],\n",
       " [16, 19, -0.008709557603406018],\n",
       " [16, 20, 0.030338154334301776],\n",
       " [17, 0, 0.12717953412249897],\n",
       " [17, 1, -0.016100106804899702],\n",
       " [17, 2, 0.11509955703692307],\n",
       " [17, 3, -0.03373943086333154],\n",
       " [17, 4, -0.0015504602720603441],\n",
       " [17, 5, 0.10967673152293243],\n",
       " [17, 6, 0.0005249884341662705],\n",
       " [17, 7, -0.0005249884341662938],\n",
       " [17, 8, -0.0044887542822744635],\n",
       " [17, 9, 0.0044887542822745295],\n",
       " [17, 10, 0.003277600079000074],\n",
       " [17, 11, -0.088254348508804],\n",
       " [17, 12, 0.0820436313318516],\n",
       " [17, 13, 0.03243413066060838],\n",
       " [17, 14, 0.01038309743097775],\n",
       " [17, 15, -0.010383097430977761],\n",
       " [17, 16, -1.0],\n",
       " [17, 17, 1.0],\n",
       " [17, 18, 0.022065160066500993],\n",
       " [17, 19, 0.008709557603406094],\n",
       " [17, 20, -0.030338154334301825],\n",
       " [18, 0, 0.015829179740975436],\n",
       " [18, 1, 0.005329339116031553],\n",
       " [18, 2, 0.04346672240754546],\n",
       " [18, 3, 0.0343210912431727],\n",
       " [18, 4, -0.020905920164527223],\n",
       " [18, 5, 0.07861359258383536],\n",
       " [18, 6, -0.08028345470353294],\n",
       " [18, 7, 0.08028345470353289],\n",
       " [18, 8, 0.006805497066597856],\n",
       " [18, 9, -0.006805497066597734],\n",
       " [18, 10, 0.04066950202960677],\n",
       " [18, 11, 0.04401519667190941],\n",
       " [18, 12, -0.08411676437991847],\n",
       " [18, 13, -0.004298056632867181],\n",
       " [18, 14, -0.07751962112239343],\n",
       " [18, 15, 0.07751962112239341],\n",
       " [18, 16, -0.022065160066500895],\n",
       " [18, 17, 0.022065160066500993],\n",
       " [18, 18, 1.0],\n",
       " [18, 19, -0.5016460069852571],\n",
       " [18, 20, -0.4491681607182965],\n",
       " [19, 0, -0.01424594560150332],\n",
       " [19, 1, -0.02704357872478651],\n",
       " [19, 2, -0.005803676615521412],\n",
       " [19, 3, 0.05914116201122619],\n",
       " [19, 4, 0.03597583895156686],\n",
       " [19, 5, 0.007196587126879432],\n",
       " [19, 6, 0.10862267070999936],\n",
       " [19, 7, -0.10862267070999938],\n",
       " [19, 8, -0.0058454149498430325],\n",
       " [19, 9, 0.005845414949843076],\n",
       " [19, 10, 0.007863407328746238],\n",
       " [19, 11, -0.004173296949485654],\n",
       " [19, 12, 0.011661246821471934],\n",
       " [19, 13, -0.012017047863104747],\n",
       " [19, 14, 0.039410434881761096],\n",
       " [19, 15, -0.03941043488176106],\n",
       " [19, 16, -0.008709557603406018],\n",
       " [19, 17, 0.008709557603406094],\n",
       " [19, 18, -0.5016460069852571],\n",
       " [19, 19, 1.0],\n",
       " [19, 20, -0.5475736604235864],\n",
       " [20, 0, -0.0005978499977315698],\n",
       " [20, 1, 0.022775703815366314],\n",
       " [20, 2, -0.0360499364513647],\n",
       " [20, 3, -0.09427867741553055],\n",
       " [20, 4, -0.016934211142743266],\n",
       " [20, 5, -0.08347305180999806],\n",
       " [20, 6, -0.03452985309134903],\n",
       " [20, 7, 0.03452985309134903],\n",
       " [20, 8, -0.0005455964689105307],\n",
       " [20, 9, 0.0005455964689103537],\n",
       " [20, 10, -0.04745963640214606],\n",
       " [20, 11, -0.03826431392741704],\n",
       " [20, 12, 0.06931972656680993],\n",
       " [20, 13, 0.016568577867511675],\n",
       " [20, 14, 0.03427916397939333],\n",
       " [20, 15, -0.034279163979393325],\n",
       " [20, 16, 0.030338154334301776],\n",
       " [20, 17, -0.030338154334301825],\n",
       " [20, 18, -0.4491681607182965],\n",
       " [20, 19, -0.5475736604235864],\n",
       " [20, 20, 1.0]]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[]\n",
    "for i in range(len(A)):\n",
    "    for j in range(len(A)):\n",
    "        l.append([i,j,A[i][j]])\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.533389</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.231109</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.890349</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0       128.0             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History  LoanAmount_log  Gender_Female  Gender_Male  Married_No  \\\n",
       "0               1.0        4.852030              0            1           1   \n",
       "1               1.0        4.852030              0            1           0   \n",
       "2               1.0        4.189655              0            1           0   \n",
       "3               1.0        4.787492              0            1           0   \n",
       "4               1.0        4.948760              0            1           1   \n",
       "..              ...             ...            ...          ...         ...   \n",
       "609             1.0        4.262680              1            0           1   \n",
       "610             1.0        3.688879              0            1           0   \n",
       "611             1.0        5.533389              0            1           0   \n",
       "612             1.0        5.231109              0            1           0   \n",
       "613             0.0        4.890349              1            0           1   \n",
       "\n",
       "     Married_Yes  ...  Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0              0  ...             1             0             0   \n",
       "1              1  ...             0             1             0   \n",
       "2              1  ...             1             0             0   \n",
       "3              1  ...             1             0             0   \n",
       "4              0  ...             1             0             0   \n",
       "..           ...  ...           ...           ...           ...   \n",
       "609            0  ...             1             0             0   \n",
       "610            1  ...             0             0             0   \n",
       "611            1  ...             0             1             0   \n",
       "612            1  ...             0             0             1   \n",
       "613            0  ...             1             0             0   \n",
       "\n",
       "     Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                     1                       0                 1   \n",
       "1                     1                       0                 1   \n",
       "2                     1                       0                 0   \n",
       "3                     0                       1                 1   \n",
       "4                     1                       0                 1   \n",
       "..                  ...                     ...               ...   \n",
       "609                   1                       0                 1   \n",
       "610                   1                       0                 1   \n",
       "611                   1                       0                 1   \n",
       "612                   1                       0                 1   \n",
       "613                   1                       0                 0   \n",
       "\n",
       "     Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                    0                    0                        0   \n",
       "1                    0                    1                        0   \n",
       "2                    1                    0                        0   \n",
       "3                    0                    0                        0   \n",
       "4                    0                    0                        0   \n",
       "..                 ...                  ...                      ...   \n",
       "609                  0                    1                        0   \n",
       "610                  0                    1                        0   \n",
       "611                  0                    0                        0   \n",
       "612                  0                    0                        0   \n",
       "613                  1                    0                        1   \n",
       "\n",
       "     Property_Area_Urban  \n",
       "0                      1  \n",
       "1                      0  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  \n",
       "..                   ...  \n",
       "609                    0  \n",
       "610                    0  \n",
       "611                    1  \n",
       "612                    1  \n",
       "613                    0  \n",
       "\n",
       "[614 rows x 21 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 分离测试集训练集\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_cv,y_train,y_cv = train_test_split(x,y,test_size=0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建逻辑回归模型.\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "pred_cv=model.predict(x_cv)\n",
    "# Accuracy_Score\n",
    "round(accuracy_score(y_cv,pred_cv),2)\n",
    "pred_test=model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of K fold 5\n",
      "Accuracy Score 0.6991869918699187\n",
      "\n",
      "2 of K fold 5\n",
      "Accuracy Score 0.7398373983739838\n",
      "\n",
      "3 of K fold 5\n",
      "Accuracy Score 0.7154471544715447\n",
      "\n",
      "4 of K fold 5\n",
      "Accuracy Score 0.7235772357723578\n",
      "\n",
      "5 of K fold 5\n",
      "Accuracy Score 0.6721311475409836\n"
     ]
    }
   ],
   "source": [
    "#构建决策树模型\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "# Let’s fit the decision tree model with 5 folds of cross validation.\n",
    "from sklearn import tree\n",
    "i=1\n",
    "skf=StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index, test_index in skf.split(x,y):\n",
    "    print('\\n{} of K fold {}'.format(i,skf.n_splits))\n",
    "    xtr, xvl= x.loc[train_index],x.loc[test_index]\n",
    "    ytr, yvl =y.loc[train_index], y.loc[test_index]\n",
    "    model=tree.DecisionTreeClassifier(random_state=1)\n",
    "    model.fit(xtr,ytr)\n",
    "    pred_test=model.predict(xvl)\n",
    "    score=accuracy_score(yvl,pred_test)\n",
    "    print('Accuracy Score',score)\n",
    "    i+=1\n",
    "    pred_test=model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of K fold 5\n",
      "Accuracy Score 0.8130081300813008\n",
      "\n",
      "2 of K fold 5\n",
      "Accuracy Score 0.8455284552845529\n",
      "\n",
      "3 of K fold 5\n",
      "Accuracy Score 0.7967479674796748\n",
      "\n",
      "4 of K fold 5\n",
      "Accuracy Score 0.8130081300813008\n",
      "\n",
      "5 of K fold 5\n",
      "Accuracy Score 0.7540983606557377\n"
     ]
    }
   ],
   "source": [
    "#构建随机森林模型\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "i=1\n",
    "skf=StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index, test_index in skf.split(x,y):\n",
    "    print('\\n{} of K fold {}'.format(i,skf.n_splits))\n",
    "    xtr, xvl= x.loc[train_index],x.loc[test_index]\n",
    "    ytr, yvl =y.loc[train_index], y.loc[test_index]\n",
    "    model=RandomForestClassifier(random_state=1,max_depth=10)\n",
    "    model.fit(xtr,ytr)\n",
    "    pred_test=model.predict(xvl)\n",
    "    score=accuracy_score(yvl,pred_test)\n",
    "    print('Accuracy Score',score)\n",
    "    i+=1\n",
    "    pred_test=model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进行网格搜索和交叉验证   \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Provide range for max_depth from 1 to 20 with an interval of 2 and from 1 to 200 with an interval of 20 for n_estimators \n",
    "paramgrid = {'max_depth': list(range(1, 20, 2)), 'n_estimators': list(range(1, 200, 20))}\n",
    "grid_search=GridSearchCV(RandomForestClassifier(random_state=1),paramgrid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=7, n_estimators=41, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=7, n_estimators=41, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=7, n_estimators=41, random_state=1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "x_train, x_cv, y_train, y_cv = train_test_split(x,y, test_size =0.3, random_state=1)\n",
    "# 拟合网格搜索\n",
    "grid_search.fit(x_train,y_train)\n",
    "# 展示最佳参数\n",
    "grid_search.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.8130081300813008\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8455284552845529\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7967479674796748\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#K-fold交叉验证，它将原始数据分成K组(K-Fold)，将每个子集数据分别做一次验证集，其余的K-1组子集数据作为训练集，这样会得到K个模型。这K个模型分别在验证集中评估结果，最后的误差MSE(Mean Squared Error)加和平均就得到交叉验证误差\n",
    "\n",
    "i=1 \n",
    "skf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True) \n",
    "for train_index,test_index in skf.split(x,y):     \n",
    "    print('\\n{} of kfold {}'.format(i,skf.n_splits))     \n",
    "    xtr,xvl = x.loc[train_index],x.loc[test_index]     \n",
    "    ytr,yvl = y[train_index],y[test_index]         \n",
    "    model = RandomForestClassifier(random_state=1, max_depth=7, n_estimators=41)     \n",
    "    model.fit(xtr, ytr)     \n",
    "    pred_test = model.predict(xvl)     \n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    print('accuracy_score',score)     \n",
    "    i+=1 \n",
    "    pred_test = model.predict(test) \n",
    "    pred2=model.predict_proba(test)[:,1]\n",
    "\n",
    "importances=pd.Series(model.feature_importances_, index=x.columns) \n",
    "importances.plot(kind='barh', figsize=(12,8))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of k fold 5\n",
      "Accuracy Score 0.6910569105691057\n",
      "\n",
      "2 of k fold 5\n",
      "Accuracy Score 0.7235772357723578\n",
      "\n",
      "3 of k fold 5\n",
      "Accuracy Score 0.7235772357723578\n",
      "\n",
      "4 of k fold 5\n",
      "Accuracy Score 0.7235772357723578\n",
      "\n",
      "5 of k fold 5\n",
      "Accuracy Score 0.6721311475409836\n"
     ]
    }
   ],
   "source": [
    "#构建Adaboost模型\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "i=1\n",
    "skf=StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in skf.split(x,y):\n",
    "    print('\\n{} of k fold {}'.format(i,skf.n_splits))\n",
    "    xtr,xvl=x.loc[train_index],x.loc[test_index]\n",
    "    ytr,yvl=y.loc[train_index],y.loc[test_index]\n",
    "    dt=DecisionTreeClassifier()\n",
    "    clf=AdaBoostClassifier(n_estimators=100,base_estimator=dt,learning_rate=1)\n",
    "    clf.fit(xtr,ytr)\n",
    "    pred_test=clf.predict(xvl)\n",
    "    score=accuracy_score(yvl,pred_test)\n",
    "    print('Accuracy Score',score)\n",
    "    i+=1\n",
    "    pred_test=clf.predict(test)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of k fold 5\n",
      "Accuracy Score 0.7398373983739838\n",
      "\n",
      "2 of k fold 5\n",
      "Accuracy Score 0.7235772357723578\n",
      "\n",
      "3 of k fold 5\n",
      "Accuracy Score 0.7642276422764228\n",
      "\n",
      "4 of k fold 5\n",
      "Accuracy Score 0.7398373983739838\n",
      "\n",
      "5 of k fold 5\n",
      "Accuracy Score 0.6967213114754098\n"
     ]
    }
   ],
   "source": [
    "#gradient boost\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "i=1\n",
    "skf=StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in skf.split(x,y):\n",
    "    print('\\n{} of k fold {}'.format(i,skf.n_splits))\n",
    "    xtr,xvl=x.loc[train_index],x.loc[test_index]\n",
    "    ytr,yvl=y.loc[train_index],y.loc[test_index]\n",
    "    dt=DecisionTreeClassifier()\n",
    "    clf=GradientBoostingClassifier(n_estimators=100,learning_rate=1)\n",
    "    clf.fit(xtr,ytr)\n",
    "    pred_test=clf.predict(xvl)\n",
    "    score=accuracy_score(yvl,pred_test)\n",
    "    print('Accuracy Score',score)\n",
    "    i+=1\n",
    "    pred_test=clf.predict(test)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a552c2ea7a7b6736ba4a8b66efcb40eda8d77c5e9d980e5b81f29aecaaefb085"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
